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E-commerce Customer Behaviour Analysis

Analysis involves:

1 Understanding the distribution and characteristics of customer demographics (e.g., age, gender, location).

2 Exploring how different types of devices are used by customers and their impact on behavior.

3Investigating the relationship between browsing time, pages viewed, items added to the cart, and actual purchases.

4 Segmenting customers based on their behavior and identifying distinct customer groups.

5 Analyzing the customer journey and identifying potential areas for improvement in the conversion funnel.

6 Assessing the impact of customer behavior on revenue generation and identifying opportunities for increasing sales and customer engagement.

# lets import libraries
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

# load data
e_commerce_df = pd.read_csv(r"C:\Users\jki\Downloads\ecommerce_customer_data.csv")
e_commerce_df.head(5)

	User_ID	Gender	Age	Location	Device_Type	Product_Browsing_Time	Total_Pages_Viewed	Items_Added_to_Cart	Total_Purchases
0	1	Female	23	Ahmedabad	Mobile	60	30	1	0
1	2	Male	25	Kolkata	Tablet	30	38	9	4
2	3	Male	32	Bangalore	Desktop	37	13	5	0
3	4	Male	35	Delhi	Mobile	7	20	10	3
4	5	Male	27	Bangalore	Tablet	35	20	8	2

# Lets chech for missing values
missing_values = e_commerce_df.isna().sum()
print(missing_values)

User_ID                  0
Gender                   0
Age                      0
Location                 0
Device_Type              0
Product_Browsing_Time    0
Total_Pages_Viewed       0
Items_Added_to_Cart      0
Total_Purchases          0
dtype: int64

# let check at the statistsical summary
e_commerce_df.describe()

	User_ID	Age	Product_Browsing_Time	Total_Pages_Viewed	Items_Added_to_Cart	Total_Purchases
count	500.000000	500.000000	500.000000	500.000000	500.000000	500.000000
mean	250.500000	26.276000	30.740000	27.182000	5.150000	2.464000
std	144.481833	5.114699	15.934246	13.071596	3.203127	1.740909
min	1.000000	18.000000	5.000000	5.000000	0.000000	0.000000
25%	125.750000	22.000000	16.000000	16.000000	2.000000	1.000000
50%	250.500000	26.000000	31.000000	27.000000	5.000000	2.000000
75%	375.250000	31.000000	44.000000	38.000000	8.000000	4.000000
max	500.000000	35.000000	60.000000	50.000000	10.000000	5.000000

# lets have a look at non-numeric column
e_commerce_df.describe(include='object')
print(e_commerce_df)

     User_ID  Gender  Age   Location Device_Type  Product_Browsing_Time  \
0          1  Female   23  Ahmedabad      Mobile                     60   
1          2    Male   25    Kolkata      Tablet                     30   
2          3    Male   32  Bangalore     Desktop                     37   
3          4    Male   35      Delhi      Mobile                      7   
4          5    Male   27  Bangalore      Tablet                     35   
..       ...     ...  ...        ...         ...                    ...   
495      496    Male   24     Mumbai      Tablet                     40   
496      497  Female   24  Hyderabad     Desktop                      8   
497      498    Male   19       Pune     Desktop                     33   
498      499    Male   28    Chennai     Desktop                      6   
499      500  Female   31    Kolkata      Mobile                     16   

     Total_Pages_Viewed  Items_Added_to_Cart  Total_Purchases  
0                    30                    1                0  
1                    38                    9                4  
2                    13                    5                0  
3                    20                   10                3  
4                    20                    8                2  
..                  ...                  ...              ...  
495                  45                    0                0  
496                  34                    4                5  
497                  41                    0                3  
498                  32                    7                4  
499                  10                   10                4  

[500 rows x 9 columns]

Now, let’s have a look at the distribution of age in the dataset

# Histogram Age
fig = px.histogram(e_commerce_df, x='Age', title='Distribution of Age')
fig.show()

Now, let’s have a look at the gender distribution:

# Bar chart for 'Gender'
gender_counts = e_commerce_df['Gender'].value_counts().reset_index()
gender_counts.columns = ['Gender', 'Count']
fig = px.bar(gender_counts, x='Gender', 
             y='Count', 
             title='Gender Distribution')
fig.show()

1 Now, let’s have a look at the relationship between the product browsing time and the total pages viewed:

# 'Product_Browsing_Time' vs 'Total_Pages_Viewed'
fig = px.scatter(e_commerce_df, x='Product_Browsing_Time', y='Total_Pages_Viewed',
                 title='Product Browsing Time vs. Total Pages Viewed', 
                 trendline='ols')
fig.show()

The above scatter plot shows no consistent pattern or strong association between the time spent browsing products and the total number of pages viewed. It indicates that customers are not necessarily exploring more pages if they spend more time on the website, which might be due to various factors such as the website design, content relevance, or individual user preferences.

2 Now, let’s have a look at the average total pages viewed by gender:

# Grouped Analysis
gender_grouped = e_commerce_df.groupby('Gender')['Total_Pages_Viewed'].mean().reset_index()
gender_grouped.columns = ['Gender', 'Average_Total_Pages_Viewed']
fig = px.bar(gender_grouped, x='Gender', y='Average_Total_Pages_Viewed',
             title='Average Total Pages Viewed by Gender')
fig.show()

3 Now, let’s have a look at the average total pages viewed by devices:

devices_grouped = e_commerce_df.groupby('Device_Type')['Total_Pages_Viewed'].mean().reset_index()
devices_grouped.columns = ['Device_Type', 'Average_Total_Pages_Viewed']
fig = px.bar(devices_grouped, x='Device_Type', y='Average_Total_Pages_Viewed',
             title='Average Total Pages Viewed by Devices')
fig.show()

4 Now, let’s calculate the customer lifetime value and visualize segments based on the customer lifetime value:

e_commerce_df['CLV'] = (e_commerce_df['Total_Purchases'] * e_commerce_df['Total_Pages_Viewed']) / e_commerce_df['Age']

e_commerce_df['Segment'] = pd.cut(e_commerce_df['CLV'], bins=[1, 2.5, 5, float('inf')],
                         labels=['Low Value', 'Medium Value', 'High Value'])

segment_counts = e_commerce_df['Segment'].value_counts().reset_index()
segment_counts.columns = ['Segment', 'Count']

# Create a bar chart to visualize the customer segments
fig = px.bar(segment_counts, x='Segment', y='Count', 
             title='Customer Segmentation by CLV')
fig.update_xaxes(title='Segment')
fig.update_yaxes(title='Number of Customers')
fig.show()

5 Now, let’s have a look at the conversion funnel of the customers:

# Funnel analysis
funnel_data = e_commerce_df[['Product_Browsing_Time', 'Items_Added_to_Cart', 'Total_Purchases']]
funnel_data = funnel_data.groupby(['Product_Browsing_Time', 'Items_Added_to_Cart']).sum().reset_index()

fig = px.funnel(funnel_data, x='Product_Browsing_Time', y='Items_Added_to_Cart', title='Conversion Funnel')
fig.show()

In the above graph, the x-axis represents the time customers spend browsing products on the e-commerce platform. The y-axis represents the number of items added to the shopping cart by customers during their browsing sessions.

6 Now, let’s have a look at the churn rate of the customers:

# Calculate churn rate
e_commerce_df['Churned'] = e_commerce_df['Total_Purchases'] == 0

churn_rate = e_commerce_df['Churned'].mean()
print(churn_rate)

0.198

A churn rate of 0.198 indicates that a significant portion of customers has churned, and addressing this churn is important for maintaining business growth and profitability.